This strategy focuses on the structure and attributes of graph data and implements a non-discriminatory augmentation for all study objects (i.e., graphs or nodes).
For example, DGI [1]} generates negative graphs by corruption functions (e.g., random shuffling of features in row-wise).
GRACE [2]} facilitates the optimization of contrastive objectives from two augmentation views through random edge removal and feature masking.
GCC [3]} uses ego-net sampling to contrst the L-hop neighbor subgraph of nodes.
GraphCL [4]} parameterizes four graph augmentation strategies (i.e., node dropping, edge perturbation, attribute masking, and subgraph sampling) to select best combination of augmentation.
